Test data generation method and device, equipment, storage medium and product

Abstract

The invention discloses a test data generation method and device, equipment, a storage medium and a product, and relates to the technical field of test.The method comprises the steps that a corresponding source data source is accessed in response to a data generation requirement and / or a strategy configuration instruction of a front-end user, and technical metadata, service metadata and sample data are collected from the source data source; according to the technical metadata, the business metadata and the sample data in the source data source, matching a pre-generated standard data creation rule, and screening out a target data creation rule; generating a corresponding data creation task based on the target data creation rule; and executing the data creation task to perform data creation, and writing data obtained by data creation into a corresponding target data source. According to the technical scheme, full-process automatic processing can be achieved, the data quality and the data creation efficiency are improved in multiple links, and the manual technical requirements and the investment time are greatly reduced.

Description

Technical Field

[0001] The present application relates to the field of testing technology, and in particular to a test data generating method, device, equipment, storage medium and product. Background Art

[0002] With the large-scale application of cloud technology in the field of financial technology, traditional business systems have been split into a large number of microservices in a more refined manner, the scale of business systems has expanded dramatically, various relational databases, NoSQL databases, Kafka, ElasticSearch, Redis and other data sources are widely used, and the test objects of functional and performance testing have increased significantly; at the same time, the current large number of software iteration cycles have limited time, and high-quality products need to be delivered quickly, and the efficiency of testing work needs to be further improved; at present, in most system testing work, a lot of time is needed to prepare test data, especially performance testing requires large quantities of data that meet business needs, which consumes more time and energy.

[0003] At present, data construction methods for different test scenarios mainly include manual data generation, various code script data generation, third-party data generation tools, etc. These methods require users to sort out the data sources of the business system, such as the technical and business attributes of database-related libraries, tables, and fields, and manually configure or implement data simulation rules and methods through code. When the business system links are complex, the system associations are dependent on many things, and there are many associations between database tables and many table data fields, the workload is very large and will consume a lot of time and energy. Moreover, since the method is only for the current test object, the reusability is low, which is not conducive to the current rapid iteration mode of software development.

[0004] In addition, in the case of large amounts of data, manually sorted techniques, business attributes, and number-generating rules are very likely to be missing, erroneous, or biased, resulting in large deviations between the test data and the actual business data model, which has a significant impact on the quality of the test. Especially in performance testing, since the amount of data is generally large, the deviation of the data model will be magnified, thereby exacerbating the risk of bias in the test result evaluation.

[0005] Furthermore, current software system development involves numerous data sources, and each type of data source needs to be adapted to the corresponding data construction method, which places high demands on the technical capabilities of relevant personnel.

[0006] Therefore, how to efficiently, quickly and in large quantities generate high-quality test data that meets business needs is the biggest and most urgent problem that needs to be solved in the current testing process. Summary of the invention

[0007] The main purpose of this application is to provide a test data generation method, device, equipment, storage medium and product, aiming to achieve how to efficiently, quickly and in large quantities generate high-quality test data that meets business needs, reducing manual technical requirements and investment time.

[0008] To achieve the above objectives, the present application proposes a test data generation method, the method comprising:

[0009] In response to the front-end user's data generation requirements and / or policy configuration instructions, access the corresponding source data source, and collect technical metadata, business metadata and sample data from the source data source;

[0010] According to the technical metadata, business metadata and sample data in the source data source, pre-generated standard number-making rules are matched to filter out target number-making rules;

[0011] Based on the target number-making rule, generating a corresponding number-making task;

[0012] The number-generating task is executed to generate numbers, and the data obtained by the number-generating is written into the corresponding target data source.

[0013] In one embodiment, before the step of responding to the front-end user's data generation requirements and / or policy configuration instructions, accessing the corresponding source data source, and collecting technical metadata, business metadata, and sample data from the source data source, the step further includes:

[0014] In response to the number-generating rule configuration instructions of the front-end user, the initial preset domain standard technical metadata and business metadata are divided into domain attribute tags, matched with preset standard data tags, and associated with default data construction rules and methods to form standard number-generating rules.

[0015] In one embodiment, before the step of generating a corresponding number-generating task based on the target number-generating rule, the step further includes:

[0016] In response to a confirmation instruction from a front-end user, confirm the screened target number-generating rule, and / or, in response to an adjustment instruction from a front-end user, adjust the label mapping relationship between the initial preset domain standard technical metadata and the business metadata, and the number-generating strategy to adjust the target number-generating rule; and / or

[0017] After the step of generating a corresponding number-generating task based on the target number-generating rule, the following steps are further included:

[0018] Generate a preset number of target sample data to confirm whether the target number-generating rule meets expectations. If so, execute the number-generating task to generate numbers.

[0019] In one embodiment, the step of executing the number-generating task to generate numbers and writing the generated data into the corresponding target data source includes:

[0020] Identify the attributes and execution strategies of various number-generating tasks, and distribute the number-generating tasks to the execution engine for number-generating according to resource conditions, and write the generated data into the corresponding target data source.

[0021] In one embodiment, the step of matching pre-generated standard number-making rules according to the technical metadata, business metadata and sample data in the source data source to filter out target number-making rules includes:

[0022] Based on the technical metadata in the source data source, matching is performed according to logical and arithmetic relationships to determine several result sets with the highest weighted similarity between the target technical attributes and the standard technical metadata set as the identified technical result sets;

[0023] According to the business metadata in the source data source, based on the Levenshtein distance algorithm and the target business attributes, several result sets with the highest weighted similarity with the preset standard business metadata attribute set and / or the metadata attribute set after user optimization feedback are calculated as the identified business result sets;

[0024] The features of the sample data in the source data source are weightedly fitted with the business result set, the technical result set, and the standard data construction method set in the standard number-making rule to obtain several result sets with the highest similarity to form a target number-making rule.

[0025] In one embodiment, the method further comprises:

[0026] In response to the number-generating rule adjustment instruction from the user, feedback optimization is performed on the target number-generating rule.

[0027] In one embodiment, the step of performing feedback optimization on the target number-generating rule in response to the number-generating rule adjustment instruction of the user comprises:

[0028] Obtain the content of the number-making rules adjusted by the user to improve the score of the user's selected rules;

[0029] Compare and identify the differences between the rules adjusted by the user and the recommended rules, and extract the difference parameter set;

[0030] Analyze the technical metadata, business metadata and number generation method attributes affected by the difference parameter set;

[0031] According to the label domain and the corresponding attributes, the domain attribute score is adjusted to generate a user-defined rule set, so that when identifying the number-making rule, weighted calculation is performed according to the corresponding score to optimize the target number-making rule.

[0032] The present application also proposes a test data generating device, the device comprising:

[0033] A collection module, used to collect technical metadata, business metadata and sample data from the source data source after accessing the source data source;

[0034] A rule identification module is used to match pre-generated standard number-making rules according to the technical metadata, business metadata and sample data in the source data source, and filter out target number-making rules;

[0035] A configuration generation module is used to configure a number generation strategy based on the target number generation rule and generate a corresponding number generation task;

[0036] The execution module is used to execute the number-generating task to generate numbers and write the generated data into the corresponding target data source.

[0037] The present application also proposes a test data generating device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the test data generating method described above.

[0038] The present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the test data generating method described above are implemented.

[0039] The present application also proposes a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the test data generating method described above are implemented.

[0040] One or more technical solutions proposed in the present application have at least the following technical effects: by responding to the number-generating needs and / or policy configuration instructions of the front-end user, accessing the corresponding source data source, collecting technical metadata, business metadata and sample data from the source data source; matching pre-generated standard number-generating rules according to the technical metadata, business metadata and sample data in the source data source, and screening out target number-generating rules; generating corresponding number-generating tasks based on the target number-generating rules; executing the number-generating tasks to perform number-generating, and writing the generated data into the corresponding target data source. The technical solution of the present application can select the corresponding data source to be connected according to the user's number-making needs and / or policy configuration, so as to realize automatic matching of multiple data sources, as well as the introduction of multi-task intelligent scheduling and execution management, realize batch number-making, greatly reduce the manual technical threshold, and shorten the intervention time required for number-making configuration; automatically generate number-making rules based on preset domain standard metadata, and the system can automatically identify number-making rules according to the technical metadata, business metadata and sample data in the source data source, which can greatly reduce the manual investment time in complex business and large data volume situations and avoid human errors; users can realize the setting of number-making needs and policies through front-end process configuration, and the system will complete subsequent operations by itself. Through data standards, multiple intelligent algorithms, and full-process automated processing, data quality and number-making efficiency are improved in multiple links, and manual technical requirements and investment time are greatly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0043] Figure 1 A schematic diagram of the system architecture involved in the test data generation method embodiment of the present application;

[0044] Figure 2 A flowchart of the first embodiment of the test data generation method of the present application is provided;

[0045] Figure 3 A flowchart of the second embodiment of the test data generation method of the present application is provided;

[0046] Figure 4 A flowchart of the third embodiment of the test data generation method of the present application is provided;

[0047] Figure 5 A complete flowchart of the test data generation method provided in the embodiment of the present application;

[0048] Figure 6 This is a schematic diagram of the module structure of the test data generating device according to an embodiment of the present application;

[0049] Figure 7 Schematic diagram of the device structure of the hardware operating environment involved in the test data generation method in the embodiment of the present application.

[0050] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0051] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0052] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0053] The main solution of the embodiment of the present application is: after accessing the source data source, collecting technical metadata, business metadata and sample data from the source data source; matching pre-generated standard number-making rules according to the technical metadata, business metadata and sample data in the source data source, and filtering out target number-making rules; generating corresponding number-making tasks based on the target number-making rules; executing the number-making tasks to perform number-making, and writing the number-making data into the corresponding target data source. The technical solution of the present application can select the corresponding data source to be connected according to the user's number-making needs and / or policy configuration, so as to realize automatic matching of multiple data sources, as well as the introduction of multi-task intelligent scheduling and execution management, realize batch number-making, greatly reduce the manual technical threshold, and shorten the intervention time required for number-making configuration; automatically generate number-making rules based on preset domain standard metadata, and the system can automatically identify number-making rules according to the technical metadata, business metadata and sample data in the source data source, which can greatly reduce the manual investment time in complex business and large data volume situations and avoid human errors; users can realize the setting of number-making needs and policies through front-end process configuration, and the system will complete subsequent operations by itself. Through data standards, multiple intelligent algorithms, and full-process automated processing, data quality and number-making efficiency are improved in multiple links, and manual technical requirements and investment time are greatly reduced.

[0054] In the existing technology, most of the data construction methods require users to sort out the data sources of the business system; when the business system links are complex, the system associations are more dependent, and the database tables are associated with many data fields, the workload is very large, which will consume a lot of time and energy; and because it is only for the current test object, the method reusability is low, which is not conducive to the current rapid iteration mode of software development. In addition, in the case of a large amount of data, the manually sorted technology, business attributes, and number-making rules are very likely to be missing, wrong, and biased, resulting in a large deviation between the test data and the actual business data model, which has a great impact on the test quality, especially in performance testing. Since the amount of data is generally large, the deviation of the data model will be magnified, thereby exacerbating the risk of deviation in the test result evaluation. The current software system development involves many data sources, and various data sources need to be adapted to the corresponding data construction methods, which requires high technical capabilities of relevant personnel.

[0055] Accordingly, the embodiments of the present application provide a solution that can efficiently, quickly, and massively generate high-quality test data that meets business needs, thereby reducing manual technical requirements and investment time.

[0056] Reference Figure 1 , Figure 1 This is a system module architecture diagram of an embodiment of the present application.

[0057] like Figure 1 As shown, the system architecture involved in the embodiment of the present application mainly includes: a front-end service module, a rule identification module, a task management module, a task execution module, a configuration management module, and a common module; the main functions of each module are as follows:

[0058] Front-end service module: used to provide front-end page service capabilities, including system data source access, user role permission management, user number creation rule configuration, user number creation strategy and other module functions, connecting the system business process to guide various users to use the system;

[0059] Rule recognition module: used to intelligently identify and match number-making rules based on standard metadata of preset fields (such as the financial field), actual metadata of data sources, and sample data, and automatically iterate and optimize the number-making rule library;

[0060] Task management module: mainly provides number-generating task management functions, responsible for the execution scheduling and cancellation of number-generating tasks, task execution progress, status, and result query, and can automatically adjust the execution strategy based on data such as execution engine resource usage and task running status;

[0061] Task execution module: used to call the number-generating task execution engine, responsible for the specific number-generating task execution, and can perform multi-concurrency and distributed scheduling execution according to the strategy;

[0062] Configuration management module: mainly used for system initialization standard metadata and construction methods, tag management, user role permission management, and execution engine environment configuration;

[0063] Public module: It is mainly used to provide some basic public capabilities for each service module. The main functions are as follows:

[0064] ① Automatic adaptation of multiple types of data sources, supporting MySQL (a relational database), TDSQL (distributed database), Oracle (a relational database management system), GaussDB (Huawei Cloud Database, a relational database), HBASE database, Kafka (a distributed message queue), ES (an open source distributed search engine, and ES is also a distributed document database), Redis (a high-performance key-value database based on memory) middleware, and different types of data sources can also be automatically adapted; the public basic module has a dedicated adaptation framework method for the currently supported database and middleware types; by collecting the attribute information of the source and target data sources and analyzing their types, when performing rule identification, it will consider giving priority to matching the rules with data source type restrictions; call the framework method corresponding to the target data source type to generate the number generation method code or script; the executor is embedded with drivers of various types of data sources, and calls the corresponding driver to generate numbers according to the generated code or script;

[0065] ② Standard number-making method code configuration, initializing standard number-making rules and number-making methods;

[0066] ③ Access to custom number-generating methods, supporting users to customize certain specific number-generating methods according to templates and other functions.

[0067] Based on the above system architecture, various method embodiments of the present application are proposed.

[0068] It should be noted that the execution subject of the method of the embodiment of the present application can be a computing service device with data processing, network communication and program running functions, such as a mobile terminal such as a tablet computer, a mobile phone or a fixed device such as a server, or a device or electronic device capable of realizing the above functions. The following embodiments are described below taking the test data generation device as an example.

[0069] The present application embodiment provides a test data method, referring to Figure 2 , Figure 2 This is a flowchart of the first embodiment of the test data generation method of the present application.

[0070] In this embodiment, the test data generating method includes steps S10 to S40, each of which is explained in detail below.

[0071] like Figure 2 As shown, the first embodiment of the present application proposes a test data generation method, the method comprising:

[0072] Step S10, in response to the front-end user's data generation requirements and / or policy configuration instructions, access the corresponding source data source, and collect technical metadata, business metadata and sample data from the source data source;

[0073] The technical solution of this embodiment can efficiently, quickly and in large quantities generate high-quality test data that meets business needs, reducing manual technical requirements and investment time.

[0074] The execution subject of this embodiment may be a test data generating device, which may be carried on a terminal or a server in the form of hardware or software.

[0075] In this embodiment, the test data generating device can be arranged in the system background, and the system background interacts with the front end to realize the generation of test data in a specific field.

[0076] The test data in this embodiment is mainly used in the financial field, but is not limited to the financial field.

[0077] As an implementation method, users can set number-making requirements and strategies through front-end process configuration, and the system will complete subsequent operations by itself.

[0078] Specifically, first, the user can perform relevant operations on the front end, enter the system operation interface, and trigger the system background to collect technical metadata, business metadata, and sample data by configuring the access source data source.

[0079] Among them, metadata refers to data that describes data. Taking relational database tables and data as an example, a record in the table represents data, and the attribute description of the data itself is metadata, such as name = "Zhang San", "Zhang San" represents data, column name-name, type-varchar, length-10, comments-customer name, etc. represent metadata. Among them, those with business meanings are business metadata, such as column names, comments, domain labels, etc., and those without business attributes, such as type, length, etc. are technical attributes.

[0080] Sample data refers to the data already in the data source.

[0081] As an implementation method, users can implement number-making requirements and strategy settings through front-end process configuration, trigger number-making requirements and / or strategy configuration instructions, and the system background responds to the front-end user's number-making requirements and / or strategy configuration instructions through a test data generation device, accesses the corresponding source data source, and collects technical metadata, business metadata and sample data from the source data source.

[0082] In the specific implementation, the front-end service module can receive the number-generating requirements and / or policy configuration instructions of the front-end user, and the configuration management module can access the corresponding source data source according to the configuration instructions, collect technical metadata, business metadata and sample data from the source data source, and then call the intelligent rule recognition module to perform number-generating rule recognition and matching.

[0083] Therefore, according to the user's number-generating needs and / or policy configuration, the corresponding data source is selected for access, so that automatic matching of multiple data sources can be achieved, batch number generation can be realized, the manual technical threshold is greatly reduced, and the intervention time required for number-generating configuration is shortened.

[0084] Step S20, matching the pre-generated standard number-making rules according to the technical metadata, business metadata and sample data in the source data source, and filtering out the target number-making rules;

[0085] This embodiment is pre-configured with standard number-making rules for a preset field.

[0086] Among them, standard number-making rules can be constructed based on the initial preset field (such as the financial field) standard technical metadata and business metadata. For example, by automatically and intelligently analyzing the technical metadata and business metadata of the data source in a specific environment, the relevant features of the sample data, matching with the preset standard data meta-tags, and automatically associating the corresponding data construction rules and methods, standard number-making rules for a specific field can be generated.

[0087] Among them, as an implementation method, the user can input the number-making rule configuration instructions in the front-end operation interface, and the system background can respond to the user's number-making rule configuration instructions through the configuration management module, divide the initial preset domain standard technical metadata and business metadata into domain attribute tags, match them with the preset standard data tags, and associate the default data construction rules and methods to form standard number-making rules.

[0088] Taking the financial field as an example, number-generating rules can be automatically generated based on standard metadata in the financial field, which can greatly reduce manual input time and avoid human errors.

[0089] Among them, the standard number-generating rules are used to provide to users for confirmation or to the system background for reference in screening number-generating rules according to current number-generating requirements.

[0090] The system backend matches the standard number-generating rule according to the technical metadata, business metadata and sample data in the source data source, and filters out the target number-generating rule.

[0091] As an implementation method, a rule recognition module can be used to intelligently match standard number-making rules and data construction methods based on technical metadata, business metadata, and sample data of the access source data source, and select highly matching rules for users to use.

[0092] Among them, when intelligently matching standard number-generating rules based on the technical metadata, business metadata, and sample data of the access data source, the rule recognition module can identify the relevant features of the technical metadata and business metadata and sample data of the source data source, such as the attribution domain attribute labels, and intelligently match the standard number-generating rules and data construction methods based on the identified attribution domain attribute labels to screen out number-generating rules with a high degree of match.

[0093] In addition, by collecting the attribute information of the source and target data sources and analyzing their types, when identifying rules, priority matching will be given to rules with data source type restrictions, thereby improving the accuracy of rule matching.

[0094] More specifically, as an implementation mode, the step of matching pre-generated standard number-making rules according to the technical metadata, business metadata and sample data in the source data source to filter out target number-making rules may include:

[0095] Based on the technical metadata in the source data source, matching is performed according to logical and arithmetic relationships to determine several result sets with the highest weighted similarity between the target technical attributes and the standard technical metadata set as the identified technical result sets;

[0096] According to the business metadata in the source data source, based on the Levenshtein distance algorithm and the target business attributes, several result sets with the highest weighted similarity with the preset standard business metadata attribute set and / or the metadata attribute set after user optimization feedback are calculated as the identified business result sets;

[0097] The Levenshtein distance, also known as the edit distance, refers to the minimum number of edit operations required to transform two strings from one to another. Permitted edit operations include replacing one character with another, inserting a character, and deleting a character. Generally speaking, the smaller the edit distance, the greater the similarity between the two strings.

[0098] The features of the sample data in the source data source are weightedly fitted with the business result set, the technical result set, and the standard data construction method set in the standard number-making rule to obtain several result sets with the highest similarity to form a target number-making rule.

[0099] As an implementation method, the screened target number-generating rules may be stored in a number-generating rule library.

[0100] Furthermore, as an implementation method, the user can also confirm or adjust the screened target number-generating rules.

[0101] Specifically, as an implementation method, after the target number-generating rules are screened out, the system background can display the target number-generating rules to the user on the front end through the rule identification module, and prompt the user to confirm or adjust them. If the user confirms, the rule identification module responds to the front-end user's confirmation instruction and confirms the screened target number-generating rules.

[0102] And / or, if the user needs to make adjustments, the adjustment strategy can be input on the front end, such as adjusting the label mapping relationship between the initial preset domain standard technical metadata and business metadata. The label mapping relationship can be the mapping relationship between the domain attribute labels of the technical metadata and business metadata and the standard number-making rules and / or data construction methods, and the adjustment strategy is sent to the rule identification module of the system background through the front-end service module via the adjustment instruction.

[0103] The rule identification module adjusts the label mapping relationship between the initial preset domain standard technical metadata and the business metadata, and the number generation strategy in response to the adjustment instruction of the front-end user, and can further adjust the target number generation rule.

[0104] And / or, as an implementation method, the system background may also generate a preset number of target sample data for providing to the user to confirm whether the target number-generating rule meets expectations. If so, the subsequent number-generating task is executed.

[0105] Therefore, the rule recognition module can be used to intelligently identify and match number-making rules based on standard metadata of preset fields (such as the financial field), actual metadata of data sources and sample data, and the number-making rule library can be automatically iterated and optimized.

[0106] Step S30, generating a corresponding number-generating task based on the target number-generating rule;

[0107] As an implementation method, a task management module may be used to generate corresponding number-generating tasks based on target number-generating rules.

[0108] Step S40, executing the number-generating task to generate numbers, and writing the generated data into the corresponding target data source.

[0109] As an implementation manner, the step of executing the number-generating task to generate numbers and writing the generated data into the corresponding target data source may include:

[0110] Identify the attributes and execution strategies of various number-generating tasks, and distribute the number-generating tasks to the execution engine for number-generating according to resource conditions, and write the generated data into the corresponding target data source.

[0111] In specific implementation, the number-generating tasks can be managed through the task management module, which is responsible for the execution scheduling and cancellation of number-generating tasks, task execution progress, status, and result query, and can automatically adjust the execution strategy based on data such as execution engine resource usage and task running status.

[0112] In addition, the number-generating task execution engine can be called through the task execution module to execute specific number-generating tasks, and multiple concurrent and distributed scheduling can be performed according to the strategy.

[0113] As an implementation method, the task management module can automatically identify various task attributes and strategies, automatically distribute the number-making tasks to the execution engine for number-making, and write them into the corresponding target data source to complete the data construction process; for tasks without dependencies, they can be automatically split and assigned to multiple execution engines for execution, thereby speeding up the execution efficiency. In this embodiment, the task management module and the task execution engine both support automatic adaptation of multiple types of data sources.

[0114] Through the above scheme, this embodiment specifically accesses the corresponding source data source in response to the number-generating demand and / or policy configuration instruction of the front-end user, collects technical metadata, business metadata and sample data from the source data source; matches pre-generated standard number-generating rules according to the technical metadata, business metadata and sample data in the source data source, and screens out target number-generating rules; generates corresponding number-generating tasks based on the target number-generating rules; executes the number-generating tasks to generate numbers, and writes the generated data into the corresponding target data source. The technical solution of the present application can select the corresponding data source to be connected according to the user's number-making needs and / or policy configuration, so as to realize automatic matching of multiple data sources, as well as the introduction of multi-task intelligent scheduling and execution management, realize batch number-making, greatly reduce the manual technical threshold, and shorten the intervention time required for number-making configuration; automatically generate number-making rules based on preset domain standard metadata, and the system can automatically identify number-making rules according to the technical metadata, business metadata and sample data in the source data source, which can greatly reduce the manual investment time in complex business and large data volume situations and avoid human errors; users can realize the setting of number-making needs and policies through front-end process configuration, and the system will complete subsequent operations by itself. Through data standards, multiple intelligent algorithms, and full-process automated processing, data quality and number-making efficiency are improved in multiple links, and manual technical requirements and investment time are greatly reduced.

[0115] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the first embodiment can refer to the above introduction, and will not be repeated in the following. Figure 3 In step S10, in response to the front-end user's data generation requirements and / or policy configuration instructions, the corresponding source data source is accessed, and the step of collecting technical metadata, business metadata and sample data from the source data source also includes step S00:

[0116] Step S00, in response to the number-generating rule configuration instruction of the front-end user, the initial preset domain standard technical metadata and business metadata are divided into domain attribute tags, matched with preset standard data tags, and associated with default data construction rules and methods to form standard number-generating rules.

[0117] Among them, standard number-making rules can be constructed based on the initial preset field (such as the financial field) standard technical metadata and business metadata. For example, by automatically and intelligently analyzing the technical metadata and business metadata of the data source in a specific environment, the relevant features of the sample data, matching with the preset standard data meta-tags, and automatically associating the corresponding data construction rules and methods, standard number-making rules for a specific field can be generated.

[0118] Among them, as an implementation method, the user can input the number-making rule configuration instructions in the front-end operation interface, and the system background can respond to the user's number-making rule configuration instructions through the configuration management module, divide the initial preset domain standard technical metadata and business metadata into domain attribute tags, match them with the preset standard data tags, and associate the default data construction rules and methods to form standard number-making rules.

[0119] Taking the financial field as an example, number-generating rules can be automatically generated based on standard metadata in the financial field, which can greatly reduce manual input time and avoid human errors.

[0120] This embodiment adopts the above scheme, specifically responds to the number-generating rule configuration instruction of the front-end user, divides the initial preset domain standard technical metadata and business metadata, and assigns them to the domain attribute tags, matches them with the preset standard data tags, and associates the default data construction rules and methods to form standard number-generating rules. After accessing the source data source, the technical metadata, business metadata and sample data are collected from the source data source; the pre-generated standard number-generating rules are matched according to the technical metadata, business metadata and sample data in the source data source, and the target number-generating rules are screened out; based on the target number-generating rules, corresponding number-generating tasks are generated; the number-generating tasks are executed to generate numbers, and the generated data are written into the corresponding target data source. The technical solution of the present application can select the corresponding data source to be connected according to the user's number-making needs and / or policy configuration, so as to realize automatic matching of multiple data sources, as well as the introduction of multi-task intelligent scheduling and execution management, realize batch number-making, greatly reduce the manual technical threshold, and shorten the intervention time required for number-making configuration; automatically generate number-making rules based on preset domain standard metadata, and the system can automatically identify number-making rules according to the technical metadata, business metadata and sample data in the source data source, which can greatly reduce the manual investment time in complex business and large data volume situations and avoid human errors; users can realize the setting of number-making needs and policies through front-end process configuration, and the system will complete subsequent operations by itself. Through data standards, multiple intelligent algorithms, and full-process automated processing, data quality and number-making efficiency are improved in multiple links, and manual technical requirements and investment time are greatly reduced.

[0121] Based on the first or second embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those of the first or second embodiment can refer to the above introduction, and will not be repeated later. Figure 4 In step S40, after executing the number-generating task to generate numbers and writing the generated data into the corresponding target data source, the method further includes step S50:

[0122] Step S50, in response to the number-generating rule adjustment instruction from the user, feedback optimization is performed on the target number-generating rule.

[0123] Compared with the above embodiments, in addition to intelligently identifying and matching number-generating rules, this embodiment can also automatically iterate and optimize the number-generating rule library according to user feedback.

[0124] Specifically, as an implementation manner, the step of performing feedback optimization on the target number-generating rule in response to the number-generating rule adjustment instruction of the user may include:

[0125] Obtain the content of the number-making rules adjusted by the user to improve the score of the user's selected rules;

[0126] Compare and identify the differences between the rules adjusted by the user and the recommended rules, and extract the difference parameter set;

[0127] Analyze the technical metadata, business metadata and number generation method attributes affected by the difference parameter set;

[0128] According to the label domain and the corresponding attributes, the domain attribute score is adjusted to generate a user-defined rule set, so that when performing number-making rule identification, weighted calculation is performed according to the corresponding score to optimize the target number-making rule, thereby realizing automatic iterative optimization of the number-making rule library.

[0129] Therefore, the number-generating rules are optimized according to user feedback, which improves the accuracy of the number-generating rules and meets the actual needs of users.

[0130] Reference Figure 5 As shown, Figure 5 A complete flowchart of the test data generation method provided in an embodiment of the present application.

[0131] The following uses a relational database table as an example to illustrate key technologies of this embodiment.

[0132] 1. Number creation rule identification

[0133] Number-generating rule identification is mainly carried out from three dimensions: business metadata, technical metadata, and sample data.

[0134] ① Business metadata identification:

[0135] Business metadata identification is mainly based on the Levenshtein distance algorithm L and the target business attribute T a 、T b 、T c , calculation and standard business metadata attribute set S s and the metadata attribute set S after user optimization feedback c The N result sets B with the highest weighted similarity topN , where N is a positive integer, and the specific calculation formula is as follows:

[0136]

[0137] B topN =Top(dSort(A)),N)

[0138] Among them, W a , W b , W c They are target business attributes T a 、T b 、T c With the standard business metadata attribute set S s and the metadata attribute set S after user optimization feedback c The weighted value between .

[0139] ② Technical metadata identification

[0140] Technical metadata identification is mainly based on matching based on logical and arithmetic relationships, and calculating the target technical attributes T c 、T d , and the standard technical metadata set S t The weighted similarity between them is obtained to obtain the weighted similarity result set C. To ensure efficiency, B topN With S t Take the intersection to reduce S t The specific calculation formula is as follows:

[0141] S t =S t ∩B topN

[0142]

[0143] Among them, W c Target Technical T c The corresponding weighted value, W d is the target technical attribute T d The corresponding weighted value;

[0144] C1 is a weighted similarity result in the weighted similarity result set C;

[0145] Stc1 is the standard technical metadata set S t Medium and target technical attributes T c The corresponding standard technical metadata;

[0146] Std1 is the standard technical metadata set S t Medium and target technical attributes T d Corresponding to a certain standard technical metadata.

[0147] ③ Intelligent analysis, identification and fitting of sample data

[0148] Sample data analysis mainly analyzes sample data form F, distribution D and other characteristics, and compares them with business result set B topN , technical result set C, standard data construction method set M weighted fitting, find the N result sets R with the highest weighted similarity topN , the specific calculation formula is as follows:

[0149]

[0150] in, It is a set of 1 to n results consisting of weighted similarities, where n is a natural number.

[0151] C1 is a technical result in the technical result set C; F1 refers to one of the sample data forms; N is the number; W c , W m They are the weighted values ​​corresponding to the technical result set C and the standard data construction method set M respectively.

[0152] 2. Optimization of number creation rule feedback

[0153] The number-making rule feedback optimization is mainly used to adjust the parameters in the rule base and optimize the intelligent recognition model through the feedback reward and punishment mechanism after the user adjusts the number-making rule. Figure 5 The process shown by the dotted line includes:

[0154] A: Obtain the content of the number-making rules adjusted by the user to improve the score of the user's selected rules;

[0155] The default standard rule score is 1. After the user adjusts it, a user-defined rule Rc will be generated. The score Sc is initially 0.8. If the user uses the rule multiple times, 0.1 is accumulated each time, and the highest score is 1:

[0156] Sc=0.8+0.1*N,Sc max =1;

[0157] B: Compare and identify the difference between the user-adjusted rule Rc and the recommended rule R, and extract the difference parameter set D R ;

[0158] D R =Rc|R

[0159] C: Analysis difference parameter set D R Affected technology, business metadata and number generation method attributes; according to the label field and corresponding attributes, adjust the field attribute score to generate a user-defined rule set S c ;

[0160]

[0161] D: When the intelligent rule recognition module identifies rules, it will perform weighted calculations based on the corresponding scores.

[0162] 3. Intelligent task scheduling and allocation

[0163] Intelligent task scheduling involves many analysis dimensions, mainly analyzing the resource consumption required for the current task, the status of tasks to be executed, the current available resources, evaluating the recommended task scheduling strategy, and estimating the task duration; during the task execution process, it will continuously monitor the task execution status and resource usage, and adjust the resource estimation parameters of this type of task; resources can be reallocated according to task priority, and high-priority tasks will strive for more resources as much as possible.

[0164] As an implementation method, the specific process of intelligent task scheduling and allocation includes:

[0165] ①Estimate of resources required for the task:

[0166] Combine the task attribute parameters such as data size s, concurrency C, target data source type t, and the resource overhead benchmark values ​​of various data source task generation tasks to calculate the resources and time required for the task;

[0167] Time consumption t1 = (s / C) / V, where V refers to the concurrent number generation rate of each type of data source, which can be preset based on experience;

[0168] Resource usage (taking CPU as an example): total number of CPU cores Cs = C*n, where n is the resource usage of a single task, which is a preset parameter and is related to the task and data source type.

[0169] ② Calculation of available resources:

[0170] Resource idleness r = ∑{c-fc(t1)}

[0171] Wherein, c represents the available resources of a single execution engine, and fc represents the resources currently in use and to be used by a single execution engine calculated based on t1.

[0172] ③ Comprehensively calculate the required resources, available resources, and task priority of the task, and give the user recommended settings for the task execution strategy; the user can fine-tune the recommended settings, and finally the system will generate the task execution strategy;

[0173] If r ≥ Cs and t a ∈a

[0174] Cfg=(C,n)

[0175] elif r <Cs

[0176] Cfg=∑(r c ,rn )

[0177] Among them, a represents the recommended set of multiple data source configurations, t represents the task data source type, and t a refers to the attribute of set a corresponding to t, C is the concurrency, n is the resource usage of a single task, r c refers to the set of idle resources, r n Refers to the estimated resource usage (Cs is one of the dimensions), and cfg refers to the specific execution strategy.

[0178] ④Task scheduling will regularly pull the execution strategy of the tasks to be executed, and intelligently schedule the execution of tasks based on the current execution engine status.

[0179] The present invention greatly reduces the work content and professional technical ability requirements of manual participation through multiple intelligent algorithms and data standards, automatic adaptation of multiple data sources, and the introduction of multi-task scheduling and execution management. If the software system has high-quality data standards, no model training is required, and manual intervention can be eliminated in the early stage to achieve full process automation.

[0180] It should be noted that in this application, there is a certain reliance on data standards in the financial field. If you need to make full use of this method system, the corresponding system must have a good data governance system to ensure that all types of metadata comply with the data standard specifications in the financial field.

[0181] It can be seen from the above scheme that the present invention automatically and intelligently analyzes the technical and business metadata and sample data characteristics of the data source in a specific environment, matches them with the preset standard data meta-tags, and automatically associates the corresponding data construction rules and methods; automatically adapts different batch number-making strategies and methods according to the requirements of data source type, data scale, etc.; users set number-making requirements and strategies through front-end process configuration, and the system completes subsequent operations by itself; at the same time, the system can feedback and optimize data standards and number-making methods according to the user's adjustment of number-making rules and strategies.

[0182] Among them, the automatic generation of number-making rules based on standard metadata in the financial field can greatly reduce manual investment time and avoid human errors; at the same time, users can optimize the number-making rules and methods by appropriately adjusting the data construction rules recognized by the system to ensure that the distribution of test data is as consistent as possible with that of real business data, further improve the quality of test data, and further improve the accuracy of intelligent recognition; through automatic adaptation of multiple data sources, and the introduction of multi-task intelligent scheduling and execution management, the manual technical threshold is greatly reduced, and the intervention time required for number-making configuration is shortened; and if the software system has high-quality data standards, or after multiple iterations of intelligent optimization of data standards, human intervention can be almost completely eliminated to achieve full process automation.

[0183] Therefore, the present invention can improve data quality and data generation efficiency in multiple links and greatly reduce manual technical requirements and investment time through data standards, multiple intelligent algorithms, and full-process automated processing.

[0184] In summary, compared with the prior art, the embodiment of the present application can automatically identify data source metadata and sample data, and intelligently match the number-making rule library constructed by financial field data standards, which greatly reduces the time invested manually in complex businesses and large data volumes, and avoids human errors; it can automatically iterate according to manual feedback, optimize the rule library, and improve the accuracy of the intelligent matching algorithm; in addition, it can automatically coordinate and schedule tasks for multi-concurrency and multi-temporal execution based on attributes such as data source type, number of number-making tasks, data volume, and number-making rule characteristics, combined with multiple dimensions such as the execution engine hardware and software environment and resource utilization; and it is simple for users to operate, the system configuration is simple, the process design is integrated, and multi-task automatic batch number-making is efficient.

[0185] Reference Figure 6 As shown, the embodiment of the present application also proposes a test data generating device, the device comprising:

[0186] The collection module 10 is used to collect technical metadata, business metadata and sample data from the source data source after accessing the source data source;

[0187] A rule identification module 20 is used to match pre-generated standard number-making rules according to the technical metadata, business metadata and sample data in the source data source, and filter out target number-making rules;

[0188] A configuration generation module 30 is used to configure a number generation strategy based on the target number generation rule and generate a corresponding number generation task;

[0189] The execution module 40 is used to execute the number-generating task to generate numbers and write the generated data into the corresponding target data source.

[0190] It should be noted that the various functional modules in the test data generating device of this embodiment can be transformed and combined according to actual conditions, for example, Figure 1 The system modules shown are associated and / or combined for the purpose of realizing the functions of the test data generating device of the present application, which will not be described in detail here.

[0191] The test data generation device provided by the present application adopts the test data generation method in the above embodiment, and can efficiently, quickly, and in large quantities generate high-quality test data that meets business needs, reducing manual technical requirements and investment time. Compared with the prior art, the beneficial effects of the test data generation device provided by the present application are the same as the beneficial effects of the test data generation method provided by the above embodiment, and other technical features in the test data generation device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0192] An embodiment of the present application further proposes a test data generating device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the test data generating method as described above.

[0193] Reference below Figure 7 , which shows a schematic diagram of the structure of a test data generating device suitable for implementing the embodiment of the present application. The test data generating device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The test data generating device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0194] like Figure 7As shown, the test data generating device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the test data generating device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the test data generation device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a test data generation device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0195] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0196] The test data generation device provided by the present application adopts the test data generation method in the above embodiment, and can efficiently, quickly, and in large quantities generate high-quality test data that meets business needs, reducing manual technical requirements and investment time. Compared with the prior art, the beneficial effects of the test data generation device provided by the present application are the same as the beneficial effects of the test data generation method provided by the above embodiment, and other technical features in the test data generation device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0197] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0198] In addition, to achieve the above objectives, the present application also proposes a storage medium, which is a computer-readable storage medium, and stores a computer program on the storage medium. When the computer program is executed by a processor, the steps of the test data generation method described above are implemented.

[0199] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0200] The computer-readable storage medium may be included in the test data generating device; or may exist independently without being assembled into the test data generating device.

[0201] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the test data generating device, the test data generating device: accesses the corresponding source data source in response to the number-generating requirements and / or policy configuration instructions of the front-end user, and collects technical metadata, business metadata and sample data from the source data source; matches pre-generated standard number-generating rules according to the technical metadata, business metadata and sample data in the source data source, and filters out target number-generating rules; generates corresponding number-generating tasks based on the target number-generating rules; executes the number-generating tasks to generate numbers, and writes the generated data into the corresponding target data source.

[0202] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0203] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0204] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0205] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned test data generation method, and can efficiently, quickly, and in large quantities generate high-quality test data that meets business needs, reducing manual technical requirements and investment time. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the test data generation method provided in the above-mentioned embodiment, and will not be repeated here.

[0206] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the test data generating method described above are implemented.

[0207] The computer program product provided by the present application can efficiently, quickly and in large quantities generate high-quality test data that meets business needs, reducing manual technical requirements and investment time. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the test data generation method provided by the above embodiment, and will not be repeated here.

[0208] One or more technical solutions proposed in the present application have at least the following technical effects: by responding to the number-generating needs and / or policy configuration instructions of the front-end user, accessing the corresponding source data source, collecting technical metadata, business metadata and sample data from the source data source; matching pre-generated standard number-generating rules according to the technical metadata, business metadata and sample data in the source data source, and screening out target number-generating rules; generating corresponding number-generating tasks based on the target number-generating rules; executing the number-generating tasks to perform number-generating, and writing the generated data into the corresponding target data source. The technical solution of the present application can select the corresponding data source to be connected according to the user's number-making needs and / or policy configuration, so as to realize automatic matching of multiple data sources, as well as the introduction of multi-task intelligent scheduling and execution management, realize batch number-making, greatly reduce the manual technical threshold, and shorten the intervention time required for number-making configuration; automatically generate number-making rules based on preset domain standard metadata, and the system can automatically identify number-making rules according to the technical metadata, business metadata and sample data in the source data source, which can greatly reduce the manual investment time in complex business and large data volume situations and avoid human errors; users can realize the setting of number-making needs and policies through front-end process configuration, and the system will complete subsequent operations by itself. Through data standards, multiple intelligent algorithms, and full-process automated processing, data quality and number-making efficiency are improved in multiple links, and manual technical requirements and investment time are greatly reduced.

[0209] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A test data generation method, characterized in that: The method comprises: In response to the front-end user's data generation requirements and / or policy configuration instructions, access the corresponding source data source, and collect technical metadata, business metadata and sample data from the source data source; According to the technical metadata, business metadata and sample data in the source data source, pre-generated standard number-making rules are matched to filter out target number-making rules; Based on the target number-making rule, generating a corresponding number-making task; The number-generating task is executed to generate numbers, and the data obtained by the number-generating is written into the corresponding target data source.

2. The method according to claim 1, characterized in that Before the step of responding to the front-end user's data generation requirements and / or policy configuration instructions, accessing the corresponding source data source, and collecting technical metadata, business metadata, and sample data from the source data source, the step also includes: In response to the number-generating rule configuration instructions of the front-end user, the initial preset domain standard technical metadata and business metadata are divided into domain attribute tags, matched with preset standard data tags, and associated with default data construction rules and methods to form standard number-generating rules.

3. The method according to claim 2, characterized in that Before the step of generating a corresponding number-generating task based on the target number-generating rule, the step further includes: In response to a confirmation instruction from a front-end user, confirm the screened target number-generating rule, and / or, in response to an adjustment instruction from a front-end user, adjust the label mapping relationship between the initial preset domain standard technical metadata and the business metadata, and the number-generating strategy to adjust the target number-generating rule; and / or After the step of generating a corresponding number-generating task based on the target number-generating rule, the following steps are further included: Generate a preset number of target sample data to confirm whether the target number-generating rule meets expectations. If so, execute the number-generating task to generate numbers.

4. The method according to claim 1, characterized in that The step of executing the number-generating task to generate numbers and writing the generated data into the corresponding target data source comprises: Identify the attributes and execution strategies of various number-generating tasks, and distribute the number-generating tasks to the execution engine for number-generating according to resource conditions, and write the generated data into the corresponding target data source.

5. The method according to claim 1, characterized in that The step of matching the pre-generated standard number-making rules according to the technical metadata, business metadata and sample data in the source data source to filter out the target number-making rules comprises: Based on the technical metadata in the source data source, matching is performed according to logical and arithmetic relationships to determine several result sets with the highest weighted similarity between the target technical attributes and the standard technical metadata set as the identified technical result sets; According to the business metadata in the source data source, based on the Levenshtein distance algorithm and the target business attributes, several result sets with the highest weighted similarity with the preset standard business metadata attribute set and / or the metadata attribute set after user optimization feedback are calculated as the identified business result sets; The features of the sample data in the source data source are weightedly fitted with the business result set, the technical result set, and the standard data construction method set in the standard number-making rule to obtain several result sets with the highest similarity to form a target number-making rule.

6. The method according to claim 1, characterized in that The method further comprises: In response to the number-generating rule adjustment instruction from the user, feedback optimization is performed on the target number-generating rule.

7. The method according to claim 6, characterized in that The step of performing feedback optimization on the target number-generating rule in response to the number-generating rule adjustment instruction of the user comprises: Obtain the content of the number-making rules adjusted by the user to improve the score of the user's selected rules; Compare and identify the differences between the rules adjusted by the user and the recommended rules, and extract the difference parameter set; Analyze the technical metadata, business metadata and number generation method attributes affected by the difference parameter set; According to the label domain and the corresponding attributes, the domain attribute score is adjusted to generate a user-defined rule set, so that when identifying the number-making rule, weighted calculation is performed according to the corresponding score to optimize the target number-making rule.

8. A test data generating device, characterized in that: The device comprises: A collection module, used to collect technical metadata, business metadata and sample data from the source data source after accessing the source data source; A rule identification module is used to match pre-generated standard number-making rules according to the technical metadata, business metadata and sample data in the source data source, and filter out target number-making rules; A configuration generation module is used to configure a number generation strategy based on the target number generation rule and generate a corresponding number generation task; The execution module is used to execute the number-generating task to generate numbers and write the generated data into the corresponding target data source.

9. A test data generating device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the test data generating method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the test data generating method according to any one of claims 1 to 7 are implemented.

11. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the test data generating method according to any one of claims 1 to 7 are implemented.

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